%0 Journal Article %1 noauthororeditor %A K, Umesh K %A Suresha, %D 2012 %J International Journal on Web Service Computing (IJWSC) %K Bag-of-Features Bag-of-Words Content-based Dense Quantization SIFT Similarity feature image measure retrieval %N 3 %P 77-84 %R 10.5121/ijwsc.2012.3307 %T Web Image Retrieval Using Visual Dictionary %U http://airccse.org/journal/jwsc/papers/3312ijwsc07.pdf %V 3 %X In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.

@article{noauthororeditor, abstract = {In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed Quantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images semantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure the distance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of 1000 generic Web images to demonstrate the effectiveness of the proposed system.}, added-at = {2020-12-17T05:56:31.000+0100}, author = {K, Umesh K and Suresha}, biburl = {https://www.bibsonomy.org/bibtex/2cc7b58360f7066fd22d8066a06f7186d/ijwsc}, doi = {10.5121/ijwsc.2012.3307}, interhash = {8e9889b353e9f15ab9f5a3344b9de159}, intrahash = {cc7b58360f7066fd22d8066a06f7186d}, issn = {0976 - 9811 (Online); 2230 - 7702 (print)}, journal = {International Journal on Web Service Computing (IJWSC)}, keywords = {Bag-of-Features Bag-of-Words Content-based Dense Quantization SIFT Similarity feature image measure retrieval}, language = {English}, month = {September}, number = 3, pages = {77-84}, timestamp = {2020-12-17T05:56:31.000+0100}, title = {Web Image Retrieval Using Visual Dictionary}, url = {http://airccse.org/journal/jwsc/papers/3312ijwsc07.pdf}, volume = 3, year = 2012 }